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在怀疑COVID-19感染的患者的识别中无监督的自然语言处理

Rildo Pinto da Silva¹, Juliana Tarossi Pollettini¹, Antonio Pazin Filho¹

¹Faculdade de Medicina de Ribeirão Preto, Universidade de São Paulo, Ribeirão Preto, Brasil.

Cadernos de saude publica

|December 6, 2023

概括

识别患有后COVID-19综合征的患者对于有效的健康促进至关重要. 无监督的自然语言处理,特别是BERTopic,在快速识别这些患者方面显示出希望,尽管它也标记了其他条件.

科学领域:

医疗信息学医疗信息学
医疗保健中的人工智能
公共卫生公共卫生

背景情况:

后COVID-19综合征需要通过健康促进计划及时干预.
有效的患者鉴定对于节省成本的项目交付至关重要,特别是在流行病期间.
传统的识别方法往往不足以进行快速的大规模查.

研究的目的:

评估无监督自然语言处理 (NLP) 主题建模的有效性,用于识别疑似COVID-19患者.
为了比较BERTopic和Word2Vec模型在识别患者以促进COVID-19后的健康方面的性能.

主要方法:

一项描述性观察性研究分析了来自私人医疗保健提供者的105,008份事先授权.
应用了无监督的NLP主题建模,包括BERTopic和Word2Vec算法.
BERTopic模型自动分组疾病,而Word2Vec则需要手动分析来识别COVID-19的主题.

主要成果:

该BERTopic模型 (超过1,000个授权/主题,没有文字处理) 识别了更严重的病例,平均成本为每份授权BRL 10,206,与人类分析相比,准确度为70%.
该模型确定1.9%的授权 (1987) 可能与健康促进计划相关,占总支出的5.4% (2030万 BRL).
BERTopic还确定了其他疾病组 (骨科,精神病,癌症),与传统方法相比,经历了病例损失.

结论:

BERTopic 作为一个有效的案例标签探索工具,可以为监督模型提供患者识别的信息.
通过NLP自动识别各种疾病群体,就健康数据隐私提出了重要的伦理考虑.
NLP方法提供了一个有前途的途径,可以提高对符合健康促进计划的患者的快速和经济有效的识别.

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